Space-based artificial intelligence (AI) is advancing rapidly with compute efficiency rapidly becoming a critical factor in measuring system capabilities. This abstract analyzes AI compute efficiency—measured in floating-point operations per second per kilogram (FLOPS/kg)—across platforms from 3U CubeSats (~4 kg) to 1000 kg satellites. Historically, larger spacecraft relied on radiation-hardened processors that had very limited compute (a couple hundred MIPs). Recent advances in edge compute have led to the ability of nanosatellites to have gigaflop to teraflop compute capabilities. This comparison of onboard edge AI compute will evaluate the tradeoff with size and compute, demonstrating the closing gap of FLOPS/kg. Space-based missions often evaluate design trade-offs such as form factor power, thermal management, and compute architecture; this work seeks to find the appropriate compute solution for workloads and then compare those to missions capable of running those loads. Early benchmarks show small-satellite GPUs achieving ~14× speedups over legacy processors. Overall, low-mass platforms in LEO are approaching the AI performance per mass of larger systems, enabling new autonomous capabilities and specialized compute workloads. At the end of this work, sample workloads for missions will be explored and will demonstrate how multiple satellites can autonomously carry out larger missions. This work seeks to demonstrate that AI capabilities including increased autonomy and onboard edge computer vision are possible on the smallest space-based platforms.

Decarbonizing aircraft transport, such as aircraft, is becoming a central priority in global efforts to reduce greenhouse gas emissions and meet long-term climate targets. This study evaluates the technical and environmental performance of hydrogen fuel-cell propulsion as an alternative to conventional jet fuel in commercial aviation. The analysis examines key aircraft design requirements, including the integration of liquid hydrogen storage tanks, fuel-cell power systems, cryogenic handling components, and electric propulsion units. It also considers the airport-side needs for hydrogen production, liquefaction, storage, and refueling, highlighting the importance of coordinated infrastructure planning for future deployment.

An environmental assessment indicates that hydrogen fuel-cell aircraft can eliminate in-flight CO₂ emissions and substantially reduce other pollutants, supporting cleaner operations and improved air quality around airports. From a technical standpoint, fuel-cell propulsion provides high energy efficiency, reduced mechanical complexity, and low noise levels, making it particularly suitable for short- and medium-range aircraft segments where electric propulsion architectures are more readily implemented.

Overall, the findings emphasize the strong potential of hydrogen fuel-cell systems to contribute to aviation decarbonization. The study offers practical insights for aircraft manufacturers, airport planners, and policymakers seeking to enable the transition toward zero-emission air transport and guide future development pathways for hydrogen-powered aviation.

The rapid expansion of satellite constellations has fundamentally transformed space operations, resulting in unprecedented volumes of onboard data and increasing dependence on limited downlink capacity and ground-based processing. This traditional ground-centric paradigm introduces latency, scalability challenges, and operational inefficiencies, particularly for time-critical applications such as anomaly detection, Earth observation, and constellation-level coordination. As the number of satellites continues to grow, these limitations pose significant risks to the sustainability and responsiveness of future space systems.

This paper proposes an edge AI-enabled framework for on-orbit intelligence that shifts key decision-making processes from ground stations to the satellites themselves. Lightweight machine learning models are deployed onboard to perform real-time data filtering, prioritization, and anomaly detection, allowing only high-value information to be transmitted to Earth. By reducing unnecessary data transmission, the proposed approach alleviates communication bottlenecks while improving operational agility.

A conceptual system architecture is presented to illustrate how edge AI can be integrated into resource-constrained space environments. Design considerations such as power limitations, radiation exposure, fault tolerance, and model update strategies are discussed to highlight practical deployment challenges. The framework emphasizes scalability and resilience, making it suitable for large and heterogeneous satellite constellations.

This study demonstrates how relocating intelligence from ground control to orbit can enhance system autonomy, reduce downlink dependency, and support more sustainable space operations. By advancing the digitalization and autonomy of space systems, this work contributes to the development of next-generation aerospace architectures and provides actionable insights for AI-enabled satellite constellation design.

This project deals with the numerical analysis of a composite cantilever beam that represents a section of a helicopter propeller blade. The analysis is carried out using ANSYS software, with the beam modeled using Fiber-Reinforced Polymer materials such as Carbon Fiber-Reinforced Polymer (CFRP) and Glass Fiber-Reinforced Polymer (GFRP). Since helicopter blades are usually pre-twisted to improve their aerodynamic performance, this study considers both the pre-twisted geometry and the anisotropic nature of composite materials. Modal analysis is performed to understand the natural frequencies and vibration modes of the beam.

Torsional vibrations are especially important in rotating blade structures, as they can lead to problems such as flutter, stall-induced vibrations, and structural instability. The interaction between axial forces and torsional motion, known as tension–torsion coupling, is examined to understand effects like blade untwisting and torsional stiffening. These phenomena are important in modern rotor blade design, where passive twist control is achieved through proper material tailoring.

The finite element model includes bending, torsion, shear deformation, and material directionality to accurately capture the dynamic behavior. The results show that pre-twist and material anisotropy significantly influence the vibration response. Overall, this study highlights how composite materials and structural tailoring can be effectively used to improve vibration control and performance in helicopter rotor blades.

The rapid increase in satellites and spacecraft has led to a critical rise in orbital debris, posing severe risks to active missions and long-term sustainability in space. Defunct satellites, spent rocket stages, and collision fragments threaten to trigger cascading collisions, making active debris mitigation an urgent priority. This project investigates a space-based laser debris mitigation system as a proactive solution to reduce orbital debris hazards.

The proposed system employs high-powered lasers mounted on space-based platforms equipped with precision pointing, advanced optics, and real-time tracking technologies. Unlike ground-based systems, space deployment eliminates atmospheric interference and enables direct access to debris across multiple orbital regimes, including LEO, MEO, and GEO. The operational principle involves directing focused laser pulses at debris surfaces to induce localized ablation, generating a recoil force that alters the object’s velocity and trajectory. This controlled perturbation enables orbital decay or relocation to less congested regions.

Integrated debris tracking, adaptive control algorithms, and optimized laser parameters ensure effective engagement across a wide range of debris sizes and materials. The system’s versatility allows mitigation of both large trackable objects and smaller high-risk fragments.

Key challenges include high power demand, energy storage, thermal management, system integration, and international regulatory concerns related to the dual-use nature of directed-energy technologies. Future work will focus on improving laser efficiency, compact power systems, advanced tracking algorithms, and simulation-based validation, alongside international collaboration.

Overall, space-based laser debris mitigation offers a scalable and effective approach to enhancing orbital safety and ensuring the long-term sustainability of space operations.

The current research aims to investigate the structural deformation, equivalent von Mises stress, and safety factor outcomes of a simplified jet trainer aircraft wing using a fluid–structure interaction (FSI) approach and optimize the structure for safe flight without lowering the structural safety margin of 1.2. The wing geometry is designed in the Ansys Design Modeler, and Aluminum alloy 2024 T3 and Aluminum alloy 7075 T6 are assigned as the material of the wing. The pressure distribution on the wing is calculated from the commercial software code Ansys Fluent and, later on, coupled with static structural analysis to solve the FSI problem. Experimental pressure coefficient data of the Onera M6 aircraft wing, tested in the NASA laboratory, is validated using Ansys Fluent. Keeping an angle of attack of 3⁰, the Mach number is varied from 0.5 to 0.8, and it was found that increasing the Mach number would increase the equivalent von Mises stress drastically, from 61.946 MPa to 236.52 MPa, passing the safety margin of the yield stress of 315 MPa. In addition, keeping the Mach number constant at 0.8 and changing the angle of attack from 0⁰ to 12⁰ would result in a rise in equivalent stress up to 411.33 MPa in linear analysis, which is higher than the yield stress, suggesting the potential plastic failure of the wing. Considering the maximum Mach number of 0.8 and angle of attack of 8⁰, an optimization is proposed to check how the thickness of the skin, ribs, and spars can be varied to bring down the stress generated on the root of the wing. A response surface optimization was conducted for satisfying the margin of safety for the wing.

VOYCE-M1 (Voice-Oriented Yielding and Communication Experiment – Mars Mission 1) is an interplanetary technology demonstration satellite intended to validate autonomous, voice-based communication from Mars-distance environments under realistic deep-space constraints. The mission is centered on the concept of “voice as data,” treating human speech not merely as an audio signal but as a structured, time-delayed communication artifact that can be processed, prioritized, and transmitted autonomously across interplanetary distances. Operating at Earth–Mars ranges, VOYCE-M1 addresses key challenges including extreme signal latency, limited bandwidth, intermittent ground contact, and strict power availability. The spacecraft incorporates an AI-enabled communication system that performs onboard voice encoding, compression, noise filtering, and content management prior to store-and-forward transmission to Earth. This architecture allows voice messages to be generated, curated, and transmitted without continuous ground control, reflecting future human–robot interaction scenarios around Mars. The transmitted voice content may include predefined human messages, autonomous system-generated narration, or mission-status audio summaries, demonstrating both cultural and operational use cases of voice in deep space. In addition to voice transmission, VOYCE-M1 supports context imaging and symbolic messaging experiments to enhance mission validation and outreach. The mission is designed for operational resilience, enabling meaningful voice and data demonstrations during interplanetary cruise and Mars-proximal operations. VOYCE-M1 serves as a precursor to future Mars communication infrastructure, offering critical insights into AI-assisted autonomy, low-power interplanetary communications, and the role of human-centric voice interaction in sustained planetary exploration. This paper outlines mission and operational planning, technical overview, mass budget, payload systems, and satellite infrastructure.

Sea-based rocket launches have become a critical capability in aerospace engineering due to their operational flexibility and expanded safety zones. However, the interaction between high-temperature supersonic exhaust and the ocean surface poses severe challenges to the structural integrity of launch platforms. Unlike land-based launches, the marine environment presents a deformable boundary with complex phase-change dynamics. This study employs the Volume of Fluid (VOF) method coupled with the k-ω SST turbulence model to conduct a comprehensive numerical investigation of these multiphase interactions. Distinct from classical internal combustion instabilities, this research reveals an external "shock–vortex–thermal" coupling mechanism formed among exhaust shockwaves, shear layer vortices, and seawater vaporization. Simulation results demonstrate that under low launch altitude conditions, rapid expansion of high-pressure steam significantly alters the shock structure, generating strong reverse jets that impose extreme thermal and mechanical loads on the platform foundation. Based on flow field topological evolution patterns, critical safety boundaries are identified. Below this altitude threshold, coupled effects trigger hazardous splashing and pressure feedback; above it, interactions effectively decouple. The parametric analysis further reveals how varying launch altitudes influence the evolution of key flow features including shock standoff distance, steam plume geometry, and pressure distribution on the platform surface. These findings provide theoretical guidance for optimizing initial launch altitude to mitigate adverse jet effects, thereby ensuring structural safety and operational stability of offshore launch platforms.

This paper introduces a computationally efficient mission planning framework tailored for the Ludi Tance-4A (LT-4A), the world's first operational GEO SAR satellite. The LT-4A's unique figure-8 ground track and large-scale imaging capabilities present complex scheduling challenges. Conventional planning methods often rely on fixed maneuver durations, leading to inefficiencies due to the satellite's evolving orbital geometry.

To address this, we propose an Orbital-Window-Aware Hybrid Genetic Algorithm (OW-HGA) integrated with an adaptive maneuver time calculation method. The approach utilizes a two-phase optimization strategy. An offline preprocessing phase exploits geosynchronous periodicity to identify "Optimal Imaging Windows" (OIWs) and dynamically calculates maneuver times based on real-time attitude adjustments. This converts a complex continuous search into a discrete set of opportunities. Subsequently, an online Genetic Algorithm determines the optimal sequence for these OIWs using rapid database lookups rather than time-consuming orbital calculations.

A case study focusing on the Yangtze River Basin validates the proposed method. Comparative results demonstrate a 19.6% reduction in total mission time against greedy algorithms and a 42% reduction in solution variance compared to standard genetic algorithms. Most notably, the framework achieves a 17-fold improvement in computational efficiency. These findings confirm the algorithm’s suitability for real-time mission planning and dynamic replanning for next-generation space-based observation systems.

Despite advances in space medicine, astronauts currently lack tools to comprehensively monitor their health in microgravity. This study aimed to demonstrate the potential of multimodal monitoring technologies to track physiological changes in space. The AURORA-HFSM experiment investigates headward fluid shifts and cerebrovascular dynamics during short-term weightlessness.

Healthy volunteers underwent multimodal monitoring during a parabolic flight campaign on the Airbus A310 Zero-G. Measurements integrated ultrasound (US), near-infrared spectroscopy (NIRS), indocyanine green (ICG) impedance tracking, and inertial measurement units (IMU) for motion synchronization. Signals were recorded continuously during each parabola (~22 s microgravity) across three gravity phases: 1.8 g pull-up (baseline), 0 g microgravity, and 1 g recovery. Each flight included 31 parabolas in three blocks, with short intermissions, providing ~11 minutes of cumulative 0 g exposure. This protocol enabled characterization of venous and arterial flow, tissue oxygenation, and thoracic fluid redistribution.

The study will provide the first synchronized multimodal dataset quantifying jugular, cerebral, and thoracic fluid dynamics during parabolic flight. Correlations between US, NIRS, and ICG indices will inform ESA research on intracranial fluid redistribution and guide the design of compact astronaut health-monitoring systems for long-duration missions.

AURORA demonstrates a feasible, safe, and operationally robust platform for multimodal physiological monitoring in microgravity. Medically certified components, low-voltage operation, mechanical robustness, and redundant data acquisition ensure ESA compliance and minimal residual risk. Expected outcomes include novel integrated biomarkers of cerebral hemodynamics, improved diagnostic accuracy, and enhanced monitoring for spaceflight and clinical applications, establishing AURORA as a translational platform bridging space physiology and astronaut health monitoring.